One could arguably compute mfc on the basis of human-annotated semantic similarity ratings.
Datasets of such ratings exist [1]}, [2]}, but they do not provide a (dis)similarity between every pair of items as is required to perform a Mantel test.
Instead, as mentioned above, we use distributional semantics models as proxy for meaning representations and use a distance between word vectors.
We consider four sets of pre-trained word embeddings:
fastText [3]}, glove 6b and glove 840b [4]},
and word2vec [5]}.
